Overview

Dataset statistics

Number of variables8
Number of observations500
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory31.4 KiB
Average record size in memory64.3 B

Variable types

Numeric7
Categorical1

Alerts

GRE Score is highly overall correlated with TOEFL Score and 6 other fieldsHigh correlation
TOEFL Score is highly overall correlated with GRE Score and 5 other fieldsHigh correlation
University Rating is highly overall correlated with GRE Score and 5 other fieldsHigh correlation
SOP is highly overall correlated with GRE Score and 5 other fieldsHigh correlation
LOR is highly overall correlated with GRE Score and 5 other fieldsHigh correlation
CGPA is highly overall correlated with GRE Score and 5 other fieldsHigh correlation
Chance of Admit is highly overall correlated with GRE Score and 6 other fieldsHigh correlation
Research is highly overall correlated with GRE Score and 1 other fieldsHigh correlation

Reproduction

Analysis started2023-01-12 09:17:39.090473
Analysis finished2023-01-12 09:17:49.478671
Duration10.39 seconds
Software versionpandas-profiling vv3.6.2
Download configurationconfig.json

Variables

GRE Score
Real number (ℝ)

Distinct50
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean316.55876
Minimum290
Maximum340
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-01-12T14:47:49.626996image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum290
5-th percentile298
Q1309
median316.55876
Q3324
95-th percentile335
Maximum340
Range50
Interquartile range (IQR)15

Descriptive statistics

Standard deviation11.103952
Coefficient of variation (CV)0.035077063
Kurtosis-0.61245754
Mean316.55876
Median Absolute Deviation (MAD)7.5587629
Skewness-0.052474881
Sum158279.38
Variance123.29775
MonotonicityNot monotonic
2023-01-12T14:47:49.782416image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
312 22
 
4.4%
324 22
 
4.4%
322 17
 
3.4%
321 17
 
3.4%
316 17
 
3.4%
327 17
 
3.4%
314 16
 
3.2%
320 16
 
3.2%
311 16
 
3.2%
316.5587629 15
 
3.0%
Other values (40) 325
65.0%
ValueCountFrequency (%)
290 2
 
0.4%
293 1
 
0.2%
294 2
 
0.4%
295 5
1.0%
296 5
1.0%
297 6
1.2%
298 10
2.0%
299 8
1.6%
300 12
2.4%
301 10
2.0%
ValueCountFrequency (%)
340 9
1.8%
339 3
 
0.6%
338 4
0.8%
337 2
 
0.4%
336 5
1.0%
335 4
0.8%
334 7
1.4%
333 4
0.8%
332 7
1.4%
331 9
1.8%

TOEFL Score
Real number (ℝ)

Distinct30
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean107.18776
Minimum92
Maximum120
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-01-12T14:47:49.922261image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum92
5-th percentile98
Q1103
median107
Q3112
95-th percentile118
Maximum120
Range28
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.0513379
Coefficient of variation (CV)0.056455496
Kurtosis-0.61664602
Mean107.18776
Median Absolute Deviation (MAD)4
Skewness0.10309764
Sum53593.878
Variance36.61869
MonotonicityNot monotonic
2023-01-12T14:47:50.033931image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
110 42
 
8.4%
105 37
 
7.4%
104 29
 
5.8%
107 28
 
5.6%
112 27
 
5.4%
106 26
 
5.2%
103 25
 
5.0%
100 24
 
4.8%
102 24
 
4.8%
99 22
 
4.4%
Other values (20) 216
43.2%
ValueCountFrequency (%)
92 1
 
0.2%
93 2
 
0.4%
94 2
 
0.4%
95 3
 
0.6%
96 6
 
1.2%
97 7
 
1.4%
98 10
2.0%
99 22
4.4%
100 24
4.8%
101 19
3.8%
ValueCountFrequency (%)
120 9
 
1.8%
119 10
 
2.0%
118 10
 
2.0%
117 8
 
1.6%
116 16
3.2%
115 11
2.2%
114 18
3.6%
113 18
3.6%
112 27
5.4%
111 20
4.0%

University Rating
Real number (ℝ)

Distinct6
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1216495
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-01-12T14:47:50.159792image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.1288019
Coefficient of variation (CV)0.36160431
Kurtosis-0.76661454
Mean3.1216495
Median Absolute Deviation (MAD)1
Skewness0.092445489
Sum1560.8247
Variance1.2741937
MonotonicityNot monotonic
2023-01-12T14:47:50.267764image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 154
30.8%
2 124
24.8%
4 103
20.6%
5 72
14.4%
1 32
 
6.4%
3.121649485 15
 
3.0%
ValueCountFrequency (%)
1 32
 
6.4%
2 124
24.8%
3 154
30.8%
3.121649485 15
 
3.0%
4 103
20.6%
5 72
14.4%
ValueCountFrequency (%)
5 72
14.4%
4 103
20.6%
3.121649485 15
 
3.0%
3 154
30.8%
2 124
24.8%
1 32
 
6.4%

SOP
Real number (ℝ)

Distinct9
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.374
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-01-12T14:47:50.377079image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.5
Q12.5
median3.5
Q34
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation0.99100362
Coefficient of variation (CV)0.29371773
Kurtosis-0.70571695
Mean3.374
Median Absolute Deviation (MAD)0.5
Skewness-0.2289724
Sum1687
Variance0.98208818
MonotonicityNot monotonic
2023-01-12T14:47:50.488074image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
4 89
17.8%
3.5 88
17.6%
3 80
16.0%
2.5 64
12.8%
4.5 63
12.6%
2 43
8.6%
5 42
8.4%
1.5 25
 
5.0%
1 6
 
1.2%
ValueCountFrequency (%)
1 6
 
1.2%
1.5 25
 
5.0%
2 43
8.6%
2.5 64
12.8%
3 80
16.0%
3.5 88
17.6%
4 89
17.8%
4.5 63
12.6%
5 42
8.4%
ValueCountFrequency (%)
5 42
8.4%
4.5 63
12.6%
4 89
17.8%
3.5 88
17.6%
3 80
16.0%
2.5 64
12.8%
2 43
8.6%
1.5 25
 
5.0%
1 6
 
1.2%

LOR
Real number (ℝ)

Distinct9
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.484
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-01-12T14:47:50.629648image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median3.5
Q34
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.92544957
Coefficient of variation (CV)0.26562847
Kurtosis-0.74574851
Mean3.484
Median Absolute Deviation (MAD)0.5
Skewness-0.14529031
Sum1742
Variance0.85645691
MonotonicityNot monotonic
2023-01-12T14:47:50.779624image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
3 99
19.8%
4 94
18.8%
3.5 86
17.2%
4.5 63
12.6%
2.5 50
10.0%
5 50
10.0%
2 46
9.2%
1.5 11
 
2.2%
1 1
 
0.2%
ValueCountFrequency (%)
1 1
 
0.2%
1.5 11
 
2.2%
2 46
9.2%
2.5 50
10.0%
3 99
19.8%
3.5 86
17.2%
4 94
18.8%
4.5 63
12.6%
5 50
10.0%
ValueCountFrequency (%)
5 50
10.0%
4.5 63
12.6%
4 94
18.8%
3.5 86
17.2%
3 99
19.8%
2.5 50
10.0%
2 46
9.2%
1.5 11
 
2.2%
1 1
 
0.2%

CGPA
Real number (ℝ)

Distinct184
Distinct (%)36.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.57644
Minimum6.8
Maximum9.92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-01-12T14:47:50.981829image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum6.8
5-th percentile7.638
Q18.1275
median8.56
Q39.04
95-th percentile9.6
Maximum9.92
Range3.12
Interquartile range (IQR)0.9125

Descriptive statistics

Standard deviation0.6048128
Coefficient of variation (CV)0.070520263
Kurtosis-0.5612784
Mean8.57644
Median Absolute Deviation (MAD)0.46
Skewness-0.026612517
Sum4288.22
Variance0.36579852
MonotonicityNot monotonic
2023-01-12T14:47:51.160297image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.76 9
 
1.8%
8 9
 
1.8%
8.12 7
 
1.4%
8.45 7
 
1.4%
8.54 7
 
1.4%
8.56 7
 
1.4%
8.65 6
 
1.2%
7.88 6
 
1.2%
9.11 6
 
1.2%
9.04 6
 
1.2%
Other values (174) 430
86.0%
ValueCountFrequency (%)
6.8 1
0.2%
7.2 1
0.2%
7.21 1
0.2%
7.23 1
0.2%
7.25 1
0.2%
7.28 1
0.2%
7.3 1
0.2%
7.34 2
0.4%
7.36 1
0.2%
7.4 1
0.2%
ValueCountFrequency (%)
9.92 1
 
0.2%
9.91 1
 
0.2%
9.87 2
0.4%
9.86 1
 
0.2%
9.82 1
 
0.2%
9.8 3
0.6%
9.78 1
 
0.2%
9.76 2
0.4%
9.74 1
 
0.2%
9.7 2
0.4%

Research
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
280 
0
220 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 280
56.0%
0 220
44.0%

Length

2023-01-12T14:47:51.285487image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-12T14:47:51.427151image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1 280
56.0%
0 220
44.0%

Most occurring characters

ValueCountFrequency (%)
1 280
56.0%
0 220
44.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 500
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 280
56.0%
0 220
44.0%

Most occurring scripts

ValueCountFrequency (%)
Common 500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 280
56.0%
0 220
44.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 280
56.0%
0 220
44.0%

Chance of Admit
Real number (ℝ)

Distinct61
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.72174
Minimum0.34
Maximum0.97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-01-12T14:47:51.578446image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.34
5-th percentile0.47
Q10.63
median0.72
Q30.82
95-th percentile0.94
Maximum0.97
Range0.63
Interquartile range (IQR)0.19

Descriptive statistics

Standard deviation0.1411404
Coefficient of variation (CV)0.19555575
Kurtosis-0.4546818
Mean0.72174
Median Absolute Deviation (MAD)0.1
Skewness-0.28996621
Sum360.87
Variance0.019920614
MonotonicityNot monotonic
2023-01-12T14:47:55.645522image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.71 23
 
4.6%
0.64 19
 
3.8%
0.73 18
 
3.6%
0.72 16
 
3.2%
0.79 16
 
3.2%
0.78 15
 
3.0%
0.76 14
 
2.8%
0.62 13
 
2.6%
0.94 13
 
2.6%
0.7 13
 
2.6%
Other values (51) 340
68.0%
ValueCountFrequency (%)
0.34 2
 
0.4%
0.36 2
 
0.4%
0.37 1
 
0.2%
0.38 2
 
0.4%
0.39 1
 
0.2%
0.42 4
0.8%
0.43 1
 
0.2%
0.44 3
0.6%
0.45 3
0.6%
0.46 5
1.0%
ValueCountFrequency (%)
0.97 4
 
0.8%
0.96 8
1.6%
0.95 5
 
1.0%
0.94 13
2.6%
0.93 12
2.4%
0.92 9
1.8%
0.91 10
2.0%
0.9 9
1.8%
0.89 11
2.2%
0.88 4
 
0.8%

Interactions

2023-01-12T14:47:47.810972image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T14:47:39.712168image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T14:47:41.109121image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T14:47:42.175056image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T14:47:43.468861image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T14:47:44.698251image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T14:47:46.407107image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T14:47:48.011183image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T14:47:39.962707image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T14:47:41.268954image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T14:47:42.375830image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T14:47:43.677128image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T14:47:44.827046image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T14:47:46.617160image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T14:47:48.181563image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T14:47:40.214506image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T14:47:41.398432image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T14:47:42.529043image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T14:47:43.888051image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T14:47:45.380018image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T14:47:46.777583image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T14:47:48.326012image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T14:47:40.385064image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T14:47:41.549415image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T14:47:42.696245image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T14:47:44.065980image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T14:47:45.573140image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T14:47:47.017351image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T14:47:48.490899image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T14:47:40.544874image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T14:47:41.717898image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T14:47:42.855364image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T14:47:44.216049image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T14:47:45.746431image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T14:47:47.180123image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T14:47:48.646028image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T14:47:40.753719image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T14:47:41.871036image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T14:47:43.029991image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T14:47:44.375191image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T14:47:45.930311image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T14:47:47.364157image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T14:47:48.842402image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T14:47:40.922417image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T14:47:42.017511image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T14:47:43.254546image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T14:47:44.527843image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T14:47:46.170799image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T14:47:47.592046image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-01-12T14:47:55.934807image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
GRE ScoreTOEFL ScoreUniversity RatingSOPLORCGPAChance of AdmitResearch
GRE Score1.0000.8170.6260.6140.5150.8210.8110.568
TOEFL Score0.8171.0000.6330.6440.5180.8040.7880.473
University Rating0.6260.6331.0000.7080.5940.6890.6890.426
SOP0.6140.6440.7081.0000.6630.7170.7030.394
LOR0.5150.5180.5940.6631.0000.6400.6440.357
CGPA0.8210.8040.6890.7170.6401.0000.8890.496
Chance of Admit0.8110.7880.6890.7030.6440.8891.0000.555
Research0.5680.4730.4260.3940.3570.4960.5551.000

Missing values

2023-01-12T14:47:49.130565image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-01-12T14:47:49.335172image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

GRE ScoreTOEFL ScoreUniversity RatingSOPLORCGPAResearchChance of Admit
0337.000000118.04.0000004.54.59.6510.92
1324.000000107.04.0000004.04.58.8710.76
2316.558763104.03.0000003.03.58.0010.72
3322.000000110.03.0000003.52.58.6710.80
4314.000000103.02.0000002.03.08.2100.65
5330.000000115.05.0000004.53.09.3410.90
6321.000000109.03.1216493.04.08.2010.75
7308.000000101.02.0000003.04.07.9000.68
8302.000000102.01.0000002.01.58.0000.50
9323.000000108.03.0000003.53.08.6000.45
GRE ScoreTOEFL ScoreUniversity RatingSOPLORCGPAResearchChance of Admit
490307.0105.02.02.54.58.1210.67
491297.099.04.03.03.57.8100.54
492298.0101.04.02.54.57.6910.53
493300.095.02.03.01.58.2210.62
494301.099.03.02.52.08.4510.68
495332.0108.05.04.54.09.0210.87
496337.0117.05.05.05.09.8710.96
497330.0120.05.04.55.09.5610.93
498312.0103.04.04.05.08.4300.73
499327.0113.04.04.54.59.0400.84